Prediction on lengths of stay in the postanesthesia care unit following general anesthesia: preliminary study of the neural network and logistic regression delling.
10.3346/jkms.2000.15.1.25
- Author:
Won Oak KIM
1
;
Hae Keum KIL
;
Jung Wan KANG
;
Hong Ro PARK
Author Information
1. Department of Anesthesiology, Yonsei University College of Medicine, Seoul, Korea. wokim@yumc.yonsei.ac.kr
- Publication Type:Original Article
- Keywords:
Recovery Room;
Postoperative Care;
Neural Network(Computer)
- MeSH:
Adult;
Anesthesia Recovery Period*;
Anesthesia, General/methods*;
Female;
Human;
Length of Stay*;
Logistic Models*;
Male;
Neural Networks (Computer)*;
Postoperative Care;
Predictive Value of Tests;
Recovery Room*;
Retrospective Studies
- From:Journal of Korean Medical Science
2000;15(1):25-30
- CountryRepublic of Korea
- Language:English
-
Abstract:
The length of stay in the postanesthesia care unit (PACU) following general anesthesia in adults is an important issue. A model, which can predict the results of PACU stays, could improve the utilization of PACU and operating room resources through a more efficient arrangement. The purpose of study was to compare the performance of neural network to logistic regression analysis using clinical sets of data from adult patients undergoing general anesthesia. An artificial neural network was trained with 409 clinical sets using backward error propagation and validated through independent testing of 183 records. Twenty-two inputs were used to find determinants and to predict categorical values. Logistic regression analysis was performed to provide a comparison. The neural network correctly predicted in 81.4% of situations and identified discriminating variables (intubated state, sex, neuromuscular blocker and intraoperative use of opioid), whereas the figure was 65.0% in logistic regression analysis. We concluded that the neural network could provide a useful predictive model for the optimization of limited resources. The neural network is a new alternative classifying method for developing a predictive paradigm, and it has a higher classifying performance compared to the logistic regression model.
